Department of Computer Science
35 Olden Street, Room 409
Princeton, NJ 08544
Email: xj [at] princeton [dot] edu
About my name:
How to pronouce it? (click the [Speaker] button at the bottom left corner to hear the sound)
In the Wade-Giles system of romanization, it is rendered as Chien-Shiung Hsiao.
In Chinese characters, it is 肖健雄 (Simplified) or 蕭健雄 (Traditional).
Jianxiong Xiao is an Assistant Professor in the Department of Computer Science at Princeton University
and the director of the Princeton Vision Group. He received his Ph.D. from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT). His research interests are in computer vision. He has been motivated by the goal of building computer systems that automatically understand visual scenes, both inferring the semantics and extracting 3D structure. Especially, he focuses on 3D Deep Learning, RGB-D Recognition and Reconstruction, Place-centric 3D Context Modeling, Graphics for Vision (Synthesis for Analysis), Deep Learning for Autonomous Driving, Large-scale Crowd-sourcing, and Petascale Big Data. His work has received the Best Student Paper Award at the European Conference on Computer Vision (ECCV) in 2012 and Google Research Best Papers Award for 2012, and has appeared in popular press in the United States. Jianxiong was awarded the Google U.S./Canada Fellowship in Computer Vision in 2012, MIT CSW Best Research Award in 2011, and two Google Research Awards in 2014 and in 2015.
More information can be found at: http://vision.princeton.edu.
- Graphics for Vision: Learning to See using Big 3D Synthetic Data (including Sliding Shapes, 3D ShapeNets, DeepDriving): PowerPoint, PDF
- Bigger than Bigger: Very Large-scale Scene Understanding (including LSUN, TurkerGaze, DeepDriving): PowerPoint, PDF
- Teaching Computers to See Using Big 3D Data (including Sliding Shapes, PanoContex, 3D ShapeNets, and SUN RGB-D): PowerPoint, PDF
- Deep Visual Learning beyond 2D Object Classification (including 3D ShapeNets, LSUN, DeepDriving): PowerPoint, PDF, Video Recording
- Google Research Awards (2015)
- Marquis Who's Who in America (2015)
- Google Research Awards (2014)
- Google Research Best Papers Award (2013)
- ECCV 2012 Best Student Paper Award (2012)
- Google Fellowship in Computer Vision (2012)
- ACCV 2012 Best Reviewer Award (2012)
- MIT CSW Best Presentation Run-up (2012)
- MIT CSW Best Research Award (2011)
- HKUST Academic Achievement Medal (2007)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016).
Panelist, National Science Foundation (NSF).
International Evaluator, Swiss National Science Foundation (SNSF).
International Journal of Computer Vision (IJCV).
- Co-chair, Large-scale Scene Understanding Challenge Workshop (LSUN) in CVPR 2015.
- Co-chair, Scene Understanding Workshop (SUNw) in CVPR 2015.
- Co-chair, Object Understanding for Interaction in ICCV 2015.
- Co-chair, Scene Understanding Workshop (SUNw) in CVPR 2014.
- Co-chair, RGB-D workshop in RSS 2014.
Reconstruction Meets Recognition Challenge (RMRC) in ECCV 2014.
- Co-chair, Scene Understanding Workshop (SUNw) in CVPR 2013.
Reconstruction Meets Recognition Challenge (RMRC) in ICCV 2013.
- PC Vice Chair,
For prospective graduate students (both PhD and Master): I plan to recruite one new PhD student each year (and 0-N Master student). You are required to apply via the department's system. *There is no need to email me. Contacting me without a good reason will NOT help. If you want to email me because of a special reason, please include [Prospective Graduate Student with Special Questions] in your email title.*
You can *indicate your interest to work in our group in your application form* and we will review your materials.
There is no requirement for previous research experience or publication in computer vision, but strong interest and solid programming skill is required.
To demonstrate your programming skill, you can put the source code and demo videos online for the systems that you build,
and put a link to them in your CV or research statement.
If you have questions about the graduate program admissions process or requirements, please see the department’s information here.
For Princeton undergrad students,
you are welcome to work with us for independent work or senior thesis.
Contact me by email with your GPA.